If you liked the early days of cloud computing, you're going to love the Internet of things (IoT) and its less-sexy cousin, machine-to-machine communications. Certainly, you'll be in elite company. Cisco is dedicating an entirely new business unit to the fledgling effort. AT&T has built two shiny new facilities dedicated to developing things like smart luggage that can locate your bags in the airport so you don't lose them. Verizon has a program aimed at transportation. Broadcom, Oracle, Samsung -- all are in the hunt. Intel says IoT technology will enable 3.8 billion more connected "things" by 2015. At an average cost of $100 per item, we're talking $380 billion (about the GDP of Austria) in just two years.

IoT fever isn't limited to IT vendors. General Electric, the 121-year-old multinational conglomerate not exactly associated with the build-your-own-market-bubble crowd, says its IoT offerings, under the rubric "Industrial Internet,"
will deliver at least a 1% improvement in efficiency to customers by way of sensor-enabled industrial equipment. Total value of that 1%? To start, $66 billion for its energy customers, $30 billion for aviation and $63 billion for healthcare.

Sounds like something enterprise IT should get in on, right? The problem is that IoT is still a mix of ill-defined terms, aspirational concepts and extreme hype (see "early days of cloud computing"). There are solid examples of the benefits of pervasive interconnectedness, but they simply don't yet merit the stratospheric projections we're seeing. To wit, Cisco pegs what it calls the "Internet of Everything" as delivering $14.4 trillion of economic value by 2022. To put that number into perspective, the total U.S. gross domestic product in 2011 was $14.9 trillion. The hole blown in the world economy by the financial industry meltdown of 2009 is generally agreed to have been about $16 trillion, and the entire debt of the U.S. government at publication time was $16.7 trillion, give or take.

Way to think big, Cisco!

To be fair, the company is putting its R&D money where its mouth is, although a dedicated unit of 500 people, mostly shifted from other company divisions, with a $200 million budget seems on the modest side for technology expected to increase asset utilization by $2.5 trillion, up employee productivity by another $2.5 trillion, boost supply chain and logistics efficiency by $2.7 trillion, improve the customers' experience (and thus their loyalty and spending) by $3.7 trillion. The remaining $3.0 trillion Cisco allocates to "innovation."

Now that we have your attention, contemplate the changes that must happen on everything from assembly lines to IPv6-ifying networks to data management, security and even public policy to get those kinds of returns. It's just damn heroic, like a Marvel comic hero but with APIs instead of an arc reactor.

The really crazy thing is that while there's some of what Alan Greenspan called "irrational exuberance" in these IoT numbers, in the long term -- probably longer than Cisco's 2022 timeline -- projections like these may be only a bit overwrought. That's mainly because the IoT takes balanced advantage of Moore's Law.

For the most part, Moore's Law so far has delivered a significant increase in computing performance paired with a lopsidedly modest decrease in system prices. The $5,000 desktop PC of the '80s now costs $500, a 90% drop, but the CPUs powering today's computers are about 10,000 times as powerful as when Intel 8088 processors roamed the Earth.

The IoT will emphasize technology that more evenly leverages Moore's Law -- performance increases will come with commensurately substantial size decreases and resulting power and cost reductions. A reasonably powerful 32-bit computer with a thin-film battery and wireless network radio can be built for a few dollars. As a result, for example, smart-home technology is starting to become reasonably priced. A smart outlet now costs about $50, compared with $5 or less for a dumb outlet without any instrumentation. The 10-1 price ratio is still enough to give most people pause; going smart will add about $2,500 to the price of an average house just for the outlets, and it will take a long time to earn that back in power savings. But once the ratio is more like 3-to-2, smart outlets will become the norm.

Unfortunately, that's only a sliver of the IoT picture, because the technological hurdles will be the easy ones to clear. Hardware (versus software, which we'll get to) is almost a solved problem.
In our outlet case, manufacturing the computer components isn't the costly part. The expense comes when you build those electronics into the outlet and add the high-power control circuitry to actually turn the outlet on and off in response to whatever triggers the homeowner sets.

This is something of a universal issue for the IoT. In the world of network-connected sensors -- which are key to not just smart outlets, but everything from health monitoring to traffic management to more-efficient manufacturing processes -- it typically costs about five times more to put the sensor in place than it does to manufacture it. That implementation cost must come down before most existing machinery will be retrofitted. Unlike cars and computers, which have four- to 10-year lifespans, we can't wait for industrial machines to hit replacement age, which can often be 20 years or more. Thorny issues like ethics and privacy must be worked through as well.

That's not to say you should ignore IoT technology. Some technologies will be slam dunks and pay off sooner rather than later. Sectors such as pharma, transportation/logistics and government already are benefiting from IoT advances (examples below), and if you're in one of these sectors, you'd better be paying close attention. If you're in a different industry, start thinking about how your company can leverage this meeting of big data, mobility, business process and the cloud.

Most IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.

Why should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.